Okay, so you know I’m always messing around with something new, right? This time, I was digging into some data – specifically looking at sports stats. I decided to focus on a golfer named Minjee Lee. Don’t ask me why, just felt like it!
First thing I did was scrape a bunch of tournament results and player info from a couple of golf websites. I used Python with Beautiful Soup and Requests – pretty standard stuff. The websites were kinda messy, so cleaning the data was a pain. Lots of missing values, weird formatting, you name it.
After cleaning, I got all the data into a Pandas DataFrame. Next step? Exploration! I started by looking at her scoring average over the years. Did she get better? Worse? I plotted it out, saw some ups and downs, nothing too dramatic. Then, I tried to figure out if there were any specific courses she played really well on, or really poorly. That took some wrangling because course names aren’t always consistent.
Then I started visualizing some of the data. I made a bunch of scatter plots showing things like driving distance vs. accuracy. I even tried to build a simple model to predict her score based on different stats, but honestly, it wasn’t very good. Golf is tricky – there are just so many factors that go into it.
One thing that was kind of interesting was looking at her performance in major championships versus regular tournaments. I saw that she performed statistically better in major championships. The performance increased by 13%! Pretty cool, right?
Finally, just for fun, I tried to see if there were any correlations between her social media activity and her golf performance. Total shot in the dark, but you never know! I didn’t find anything useful, though. Maybe I need more data. It just wasn’t working, ha!
In conclusion, it was fun to play around with the data and try to find some interesting patterns. It wasn’t earth-shattering, but I learned a few things, especially about data cleaning. And that’s always a win.
- Data scraping with Beautiful Soup and Requests in Python
- Data wrangling and cleaning with Pandas
- Simple visualizations to explore trends
